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Olfactory conditioning of mosquitoes may have important implications for vector-pathogen-host dynamics. If mosquitoes learn about specific host attributes associated with pathogen infection, it may help to explain the heterogeneity of biting and disease patterns observed in the field. Sugar-feeding is a requirement for survival in both male and female mosquitoes. It provides a starting point for learning research in mosquitoes that avoids the confounding factors associated with the observer being a potential blood-host and has the capability to address certain areas of close-range mosquito learning behavior that have not previously been described. This study was designed to investigate the ability of the southern house mosquito, Culex quinquefasciatus Say to associate odor with a sugar-meal with emphasis on important experimental considerations of mosquito age (1.2 d old and 3-5 d old), sex (male and female), source (laboratory and wild), and the time between conditioning and testing (<5 min, 1 hr, 2.5 hr, 5 hr, 10 hr, and 24 hr). Mosquitoes were individually conditioned to an odor across these different experimental conditions. Details of the conditioning protocol are presented as well as the use of binary logistic regression to analyze the complex dataset generated from this experimental design. The results suggest that each of the experimental factors may be important in different ways. Both the source of the mosquitoes and sex of the mosquitoes had significant effects on conditioned responses. The largest effect on conditioning was observed in the lack of positive response following conditioning for females aged 3-5 d derived from a long established colony. Overall, this study provides a method for conditioning experiments involving individual mosquitoes at close range and provides for future discussion of the relevance and broader questions that can be asked of olfactory conditioning in mosquitoes.
Pubmed ID: 21887384 RIS Download
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NEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. NEURON has benefited from judicious revision and selective enhancement, guided by feedback from the growing number of neuroscientists who have used it to incorporate empirically-based modeling into their research strategies. NEURON's computational engine employs special algorithms that achieve high efficiency by exploiting the structure of the equations that describe neuronal properties. It has functions that are tailored for conveniently controlling simulations, and presenting the results of real neurophysiological problems graphically in ways that are quickly and intuitively grasped. Instead of forcing users to reformulate their conceptual models to fit the requirements of a general purpose simulator, NEURON is designed to let them deal directly with familiar neuroscience concepts. Consequently, users can think in terms of the biophysical properties of membrane and cytoplasm, the branched architecture of neurons, and the effects of synaptic communication between cells. * helps users focus on important biological issues rather than purely computational concerns * has a convenient user interface * has a user-extendable library of biophysical mechanisms * has many enhancements for efficient network modeling * offers customizable initialization and simulation flow control * is widely used in neuroscience research by experimentalists and theoreticians * is well-documented and actively supported * is free, open source, and runs on (almost) everything
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